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Probabilistic faster R-CNN with stochastic region proposing: Towards object detection and recognition in remote sensing imagery
- Source :
- Neurocomputing. 459:290-301
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Object detection is one of the most important tasks involved in intelligent agriculture systems, especially in pest detection. This paper focuses on a most devastated agricultural disaster: grasshopper plagues. Grasshopper detection and monitoring is of paramount importance in preventing grasshopper plagues. This paper proposes a probabilistic faster R-CNN algorithm with stochastic region proposing, where a probabilistic region proposal network, an image classification network, and an object detection network are integrated to detect and locate grasshoppers. More specifically, in the proposed framework, the probabilistic region proposal network considers attributes (e.g. size, shape) of region proposals and the image classification network identifies the existence of grasshoppers while the object detection network scores recognition confidence for a region proposal. By integrating these three networks, the uncertainty can be passed from end to end, and the final confidence is obtained for each region proposal can be explicitly quantified. To enhance algorithm robustness, a stochastic region proposing algorithm is developed to screen region proposals rather than using a predetermined threshold. The proposed algorithm is validated by recently collected grasshopper datasets. The experimental results demonstrate that the proposed algorithm not only outperforms competing algorithms in terms of average precision (0.91), average missed rate (0.36), and maximum F1-score (0.9263), but also reduces the false positive rate of recognising the existence of grasshoppers in an open field.
- Subjects :
- Contextual image classification
Computer science
Cognitive Neuroscience
Probabilistic logic
Region proposal
Algorithm robustness
Mixture model
computer.software_genre
Object detection
Computer Science Applications
End-to-end principle
Artificial Intelligence
Data mining
False positive rate
computer
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 459
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........135f117f68e63532fc6b7a2f4e9af1dd